{"id":"W1734993443","doi":"10.1109/pacrim.1991.160805","title":"Applying uncertainty principles in environmental modelling","year":2002,"lang":"en","type":"article","venue":"","topic":"Bayesian Modeling and Causal Inference","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"","keywords":"Bayesian network; Inference; Computer science; Representation (politics); Watershed; Bayesian inference; Artificial intelligence; Data mining; Operations research; Machine learning; Bayesian probability; Mathematics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001271587,0.0000879768,0.00008427077,0.00005823738,0.00005298353,0.00006743234,0.0003962198,0.00003927506,0.00008049429],"category_scores_gemma":[0.000002501989,0.00008022279,0.00002585596,0.0001111053,0.00001844139,0.0002415015,0.0001213764,0.0001155628,0.0001368453],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004458602,"about_ca_system_score_gemma":0.000005144595,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004623305,"about_ca_topic_score_gemma":0.000006958333,"domain_scores_codex":[0.9991671,0.00002182791,0.0001650664,0.0002747602,0.000149534,0.0002217328],"domain_scores_gemma":[0.9996293,0.00002681759,0.00002401152,0.0002605211,0.000003362071,0.00005597627],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[7.753948e-7,0.00008119599,0.0007636953,0.000003583574,0.000002648752,0.0000107887,0.0005780066,0.8506786,0.0003152785,0.07830434,0.00004163165,0.06921948],"study_design_scores_gemma":[0.00009441476,0.00001025947,0.00003649663,0.000008751735,4.934278e-7,0.000004095412,0.0000249141,0.9957419,0.0001488586,0.002853198,0.0009639515,0.0001126761],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02741248,0.0001236362,0.9627606,0.0001556767,0.00004267833,0.00009918066,3.759143e-7,0.00009598047,0.009309369],"genre_scores_gemma":[0.933998,0.00005769623,0.06511167,0.0002079973,0.00001375849,0.00003396138,5.027446e-7,0.000003894701,0.0005724718],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9065856,"threshold_uncertainty_score":0.327139,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06996062089734079,"score_gpt":0.2259229766297544,"score_spread":0.1559623557324137,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}